The present invention, in some embodiments thereof, relates to data and, more specifically, but not exclusively, to visualization of designs in an objective space.
In real-world problems decision-makers are often imposed to multiple conflicting objectives, and a large solution space with many possible options to consider. A long-studied topic in the area of multi-criterion decision making (MCDM) is how to assist decision-makers in reaching better decisions in a more efficient way. The multi-criterion decision making process typically involves two mathematical spaces:
(a) the design space which comprises the set of defining variables of the candidate solutions, sometimes referred to as the decision variables; and
(b) the objective space, which constitutes the mapping of each candidate design point (i.e., a collection of design variables in the design space) to the values quantifying their quality with respect to the given objective functions (i.e., a vector of objective function values), the space where optimality is defined and where tradeoffs are explored.
Decision-makers typically examine the objective space and consequently select an option that meets their criteria; however, in most practical scenarios, the amount of options is too large to be examined by a human, and decision-makers aspire to examine a limited set of options. The task of visualizing solutions, such as Pareto-optimal solutions in an objective space, such as Pareto Frontier objective space, is based upon multivariate visualization.